用自适应融合频域与空间特征，提升少样本脑膜瘤分类精度
Meningioma Analysis and Diagnosis using Limited Labeled Samples
- 设计自适应加权融合架构，动态调节频域与空间特征贡献
- 在三个数据集上优于现有方法，少样本下准确率显著提升
- 适合医疗影像少样本学习研究者，尤其关注脑膜瘤诊断
脑膜瘤的生物学行为和治疗反应与其分级密切相关，准确诊断对治疗方案制定和预后评估至关重要。我们发现，空间-频率域特征的加权融合显著影响脑膜瘤分类性能，且离散小波变换提取的不同频带在不同图像中的贡献差异显著。为此，提出一种具有自适应权重的特征融合架构，用于少样本脑膜瘤学习。为验证方法有效性，构建了一个新的脑膜瘤MRI数据集。实验结果表明，该方法在三个数据集上均优于现有最先进方法。代码将公开于：https://github.com/ICL-SUST/AMSF-Net
原文摘要 · Abstract (English)
The biological behavior and treatment response of meningiomas depend on their grade, making an accurate diagnosis essential for treatment planning and prognosis assessment. We observed that the weighted fusion of spatial-frequency domain features significantly influences meningioma classification performance. Notably, the contribution of specific frequency bands obtained by discrete wavelet transform varies considerably across different images. A feature fusion architecture with adaptive weights of different frequency band information and spatial domain information is proposed for few-shot meningioma learning. To verify the effectiveness of the proposed method, a new MRI dataset of meningiomas is introduced. The experimental results demonstrate the superiority of the proposed method compared with existing state-of-the-art methods in three datasets. The code will be available at: https://github.com/ICL-SUST/AMSF-Net
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